构建显微精索手术治疗精索疼痛的术后疼痛缓解预测模型

doi:10.19723/j.issn.1671-167X.2024.04.017

摘要/Abstract

摘要：

目的: 探讨影响显微精索手术疗效的相关因素，构建采用显微精索手术治疗精索疼痛的术后疼痛缓解预测模型。方法: 回顾性分析2015年10月至2023年4月就诊于北京大学人民医院泌尿外科的324例精索疼痛患者的临床资料，患者均行显微精索手术。采用Lasso回归筛选临床预测变量，通过Logistic回归构建显微精索手术后精索神经痛有效缓解的临床预测模型并绘制列线图，采用Bootstrap法进行模型的内部验证，评估模型的预测能力和临床实用性。结果: 采用Lasso回归和Logistic回归筛选出年龄(OR=1.081, 95%CI: 1.033~1.137, P=0.001)、患者健康问卷抑郁量表(patient health questionnaire-9, PHQ-9)评分(OR=0.906, 95%CI: 0.768~1.048, P=0.04)、广泛性焦虑障碍量表(generalized anxiety disorder-7，GAD-7)评分(OR=0.759, 95%CI: 0.627~0.900, P=0.003)、慢性疼痛时间(OR=1.070，95%CI: 1.028~1.127, P=0.002)和曲张精索静脉内径(OR=0.150，95%CI: 0.072~0.282, P < 0.001)这5个临床指标是精索静脉曲张相关精索疼痛患者术后疗效的独立影响因素；年龄(OR=1.092, 95%CI: 1.033~1.163, P=0.003)、PHQ-9(OR=0.837, 95%CI: 0.717~0.959, P=0.016)、GAD-7(OR=0.785, 95%CI: 0.648~0.922, P=0.007)和慢性疼痛时间(OR=0.925, 95%CI: 0.876~0.971, P=0.002)是特发性精索疼痛患者术后疗效的独立影响因素。基于上述临床治疗构建模型并绘制列线图，受试者工作特征曲线下面积分别为0.909(精索静脉曲张组)和0.913(特发性精索疼痛组)，模型内部验证的一致性指数分别为0.93(精索静脉曲张组)和0.94(特发性精索疼痛组)，说明该模型具有良好的预测能力。结论: 基于年龄、疼痛病程时间、PHQ-9评分、GAD-7评分和曲张的精索静脉内径构建的术后疗效预测模型具备良好的预测能力和临床适用性，可用于临床实践。

关键词: 精索, 疼痛, 精索静脉曲张, 去神经支配, 预测模型

Abstract:

Objective: To explore the relevant factors affecting the efficacy of microscopic spermatic cord surgery and build a predictive model for postoperative pain relief. Methods: A retrospective analysis was conducted on the clinical data of 324 patients with spermatic cord pain who visited the Department of Urology at Peking University People's Hospital between October 2015 and April 2023. This cohort included 212 patients with varicocele-related spermatic cord pain and 112 patients with idiopathic spermatic cord pain. All the patients underwent microsurgical procedures: varicocele-related pain was treated with microsurgical varicocelectomy, and idiopathic pain was treated with microsurgical denervation of the spermatic cord. The patients were categorized into effective and ineffective groups based on whether their pain had decreased by more than 50% six months post-surgery compared with pre-surgery levels. Baseline data were preliminarily screened for clinical indicators using t tests and univariate analysis. Clinical predictor variables [age, duration of pain, diameter of varicocele, patient health questionnaire-9 (PHQ-9) score, generalized anxiety disorder-7 (GAD-7) score] were selected using Lasso regression. A clinical prediction model for effective pain relief following microscopic spermatic cord surgery was constructed using Logistic regression and presented as a nomogram. The model's internal validation was performed using the bootstrap method. Its predictive power and clinical utility were evaluated through the concor-dance index, the area under the receiver operating characteristic curve, and calibration plots. Results: Post-microscopic varicocele ligation, 156 patients (73.58%) experienced significant pain relief, as did 94 patients (83.93%) following microscopic denervation. Independent predictors for postoperative outcomes included age, PHQ-9 score, GAD-7 score, chronic pain duration, and varicocele diameter, differing slightly between varicocele-related and idiopathic pain groups. The models demonstrated excellent predictive ability, with areas under the curve of 0.909 and 0.913 for varicocele and idiopathic groups, respectively, and high concordance indices. Conclusion: The postoperative efficacy prediction model based on age, pain duration, PHQ-9 score, GAD-7 score, and varicocele diameter has good predictive ability and clinical applicability, and can be used in clinical practice.

Key words: Spermatic cord, Pain, Varicocele, Denervation, Predictive model

中图分类号:

R697.24

何海龙,李清,徐涛,张晓威. 构建显微精索手术治疗精索疼痛的术后疼痛缓解预测模型[J]. 北京大学学报（医学版）, 2024, 56(4): 646-655.

Hailong HE,Qing LI,Tao XU,Xiaowei ZHANG. Construction of a predictive model for postoperative pain relief after microscopic spermatic cord surgery for spermatic cord pain[J]. Journal of Peking University (Health Sciences), 2024, 56(4): 646-655.

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Influencing factors	Regression coefficient	Standard error	Z	P	OR	OR (95%CI)
Age/years	0.078	0.024	3.240	0.001	1.081	1.033-1.137
Pain course time/months	0.071	0.022	3.080	0.002	1.070	1.029-1.127
PHQ-9	-0.100	0.080	-1.229	0.040	0.906	0.768-1.048
GAD-7	-0.275	0.092	-2.990	0.003	0.759	0.627-0.900
Varicocele vein diameter/mm	-1.897	0.346	-5.476	< 0.001	0.150	0.072-0.282
Intercept	3.875	1.415	2.737	0.006	48.175	3.331-894.121

Influencing factors	Regression coefficient	Standard error	Z	P	OR	OR (95%CI)
Age/years	0.088	0.030	2.932	0.003	1.092	1.033-1.163
Pain course time/months	-0.078	0.026	-3.039	0.002	0.925	0.876-0.971
PHQ-9	-0.178	0.074	-2.418	0.016	0.837	0.717-0.959
GAD-7	-0.242	0.090	-2.700	0.007	0.785	0.648-0.922
Intercept	2.829	1.508	1.876	0.061	16.930	1.025-420.954

Influencing factors	AUC value	Sensitivity	Specificity	Youden index	Cut off
Prediction model	0.909	0.882	0.833	0.715	0.645
Age/years	0.684	0.546	0.900	0.446	46.000
Time of pain course/months	0.723	0.849	0.550	0.399	17.000
PHQ-9	0.702	0.683	0.658	0.341	5.000
Varicocele vein diameter/mm	0.769	0.783	0.678	0.461	3.000
GAD-7	0.763	0.867	0.612	0.479	4.000

Influencing factors	AUC value	Sensitivity	Specificity	Youden index	Cut off
Prediction model	0.913	0.863	0.906	0.769	0.734
Age/years	0.772	0.650	0.844	0.494	43.000
Pain course time/months	0.794	0.844	0.600	0.444	24.000
PHQ-9	0.850	0.906	0.625	0.531	6.000
GAD-7	0.738	0.813	0.713	0.525	7.000